Alex Ye | Searcharxiv

arXiv2026

Nemotron 3 Ultra: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning

We introduce Nemotron 3 Ultra, a 550 billion total and 55 billion active parameter Mixture-of-Experts Hybrid Mamba-Attention language model. We pre-trained Nemotron 3 Ultra on 20 trillion text tokens, then extended the context length to 1M tokens, and post-trained using Supervised Fine Tuning (SFT), Reinforcement Learning (RL), and Multi-teacher On-Policy Distillation (MOPD). Nemotron 3 Ultra is our most capable model yet, employing multiple key technologies - LatentMoE, Multi Token Prediction (MTP), NVFP4 pre-training, multi-environment RLVR, MOPD, and reasoning budget control. Nemotron 3 Ultra achieves up to ~6x higher inference throughput as compared to state-of-the-art publicly available LLMs while attaining on-par accuracy. The state-of-the-art accuracy, high inference throughput, and 1M token context length make Nemotron 3 Ultra ideal for long-running autonomous agentic tasks. We open-source the base, post-trained, and quantized checkpoints, along with the training data and recipe on HuggingFace.

cs.CL↗

arXiv2025

Unveiling Usability Challenges in Web Privacy Controls

With the increasing concerns around privacy and the enforcement of data privacy laws, many websites now provide users with privacy controls. However, locating these controls can be challenging, as they are frequently hidden within multiple settings and layers. Moreover, the lack of standardization means these controls can vary widely across services. The technical or confusing terminology used to describe these controls further complicates users' ability to understand and use them effectively. This paper presents a large-scale empirical analysis investigating usability challenges of web privacy controls across 18,628 websites. While aiming for a multi-scenario view, our automated data collection faced significant hurdles, particularly in simulating sign-up and authenticated user visits, leading to more focused insights on guest visit scenarios and challenges in automated capture of dynamic user interactions. Our heuristic evaluation of three different user visit scenarios identifies significant website usability issues. Our results show that privacy policies are most common across all visit scenarios, with nudges and notices being prevalent in sign-up situations. We recommend designing privacy controls that: enhance awareness through pop-up nudges and notices; offer a table of contents as navigational aids and customized settings links in policies for more informed choice; and ensure accessibility via direct links to privacy settings from nudges.

cs.CR↗